Natural Language Processing in Google Cloud Platform
Natural Language Processing can help to improve communication and influence by improving the ability to read and predict the responses of end-users as well as to predict the future using Deep Learning and Future AI.
Natural language processing uses Machine Learning to unravel the meaning as well as the structure of the text. It has become one of the most heavily researched subjects in the field of artificial intelligence. Natural Language API is an easy to use interface to a set of powerful NLP models which have been pre-trained initially to perform various tasks.
Features of Google Natural Language Processing
Natural Language Processing API has several methods for performing analysis and annotation on the text or rather on the natural voice. In NLP every level of analysis provides valuable information for language understanding, social media sentiment, preference and more.
The Methods of NLP are
Sentiment Analysis: Pre-defined datasets provide the text that identifies the prevailing emotional opinion within the text, especially to determine the end user’s attitude as positive, negative, or neutral
Entity Analysis: It checks the pre-defined text for known entities and common pairs among the total text and gives the accurate result
Entity Sentiment Analysis: It checks the end user’s willingness towards the writing style and attitude
Syntactic Analysis: The dataset model extracts linguistic information and breaks the user’s text into a series of sentences and tokens which provides further analysis on those tokens using Python Datasets and GO
Content Classification: The dataset analyzes the text content and returns a homogeneous category for the content
Advantages to NLP
Multimedia and Multilingual Support: The predefined data set model provides a wide variety of languages which in turn provides the best neuropsychological activities of the end-user
Extract Key Document: The NLP dataset model extracts the data from a text or speech source which is most important to read the activities of the end-user
Content Classification Relationship Graphs: After extracting the text the model set then analyzes the content and finds the relationship amongst the predefined datasets
